INFORMATION SYSTEM FOR DATA PROCESSING IN SPORTS USING THE RANDOM FOREST METHOD

Abstract

A huge amount of data is collected and generated in modern sports. This data can be used to improve athletes' performance, make more informed coaching and strategic decisions, and increase fan engagement. However, processing, analyzing, and interpreting this data can be challenging. This article is devoted to the development of an information system for data processing in the sports sector using the random forest method. The system aims to ensure efficient collection, processing, and analysis of large amounts of data generated during sports competitions, training, and interaction with fans and other stakeholders.

Research methods. This article proposes an information system (IS) for data processing in the sports industry using the Random Forest (RF) method. As one of the machine learning methods, it is well suited for working with large amounts of data and complex classification and prediction tasks. The proposed IS consists of three main components. The data collection module accumulates data from various sources such as sensors, GPS trackers, websites, and social networks. The data processing module cleans, normalizes, and transforms the data to prepare it for analysis. The data analysis module uses the RF method to analyze data, predict outcomes, identify patterns, and make decisions.

The conducted research has shown that the proposed IS can be an effective tool for predicting the results of sports competitions with high accuracy, identifying patterns in the data that can be useful for coaches and athletes to improve their training and strategy, personalizing training programs and recommendations for athletes, increasing the level of fan engagement by providing them with personalized content and forecasts.

The proposed IS based on the random forest method is a powerful tool for processing and analyzing data in the sports industry. Its use can lead to improved athletes' performance, more informed coaching and strategic decisions, and increased fan engagement.

One of the most powerful and accurate machine learning methods, the random forest method, allows for reliable analysis and forecasting based on various types of data, including player statistics, match results, physiological indicators, and fan behavior data. The article describes the stages of creating an information system: from data collection to data processing, storage, and analysis.